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As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Lead GTM technical engagements with data-platform ISVs to design architectures, build POCs, benchmarks, and enablement for NVIDIA acceleration across analytics, databases, and GenAI workflows. Create reference assets, demos, and training while partnering with product, engineering, sales, and customers.

Invent and research AI-native numerical algorithms that integrate ML with classical solvers on GPUs. Develop learned preconditioners, AI-guided multigrid, differentiable solver components, and solver-in-the-loop pipelines. Define numeric evaluation metrics, collaborate across research and product teams, and help set NVIDIA's applied research agenda for solver intelligence.

The role focuses on developing and maintaining continuous integration systems for NVIDIA's AI software ecosystem, ensuring optimal performance and quality of software stacks.

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.

The role involves optimizing GPU performance for neural reconstruction workflows, analyzing bottlenecks, and collaborating with engineers to enhance training and rendering systems.

Create developer-facing technical materials and demos for NVIDIA Omniverse libraries, SDKs, and APIs. Produce tutorials, reference architectures, blogs, videos, and launch collateral to drive developer adoption for simulation, rendering, robotics, synthetic data, and industrial digitalization workflows. Collaborate with engineering, product, marketing, and sales to identify gaps in the developer journey and translate platform capabilities into clear technical narratives and hands-on content.

Lead architecture for cloud-networking and design system architectures for DPUs and NICs. Build end-to-end solutions from application to hardware, write architecture specifications, evaluate new technologies, develop POCs, and collaborate globally with SW/HW teams and customers.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.